A homing landing point planning method for parafoil systems based on terrain and features

Abstract

The present invention discloses a method for planning a homing landing point for a parafoil system based on terrain and features, comprising: selecting nominal landing points based on a terrain analysis model; selecting a number of two-dimensional feature points from all nominal landing points; sorting all two-dimensional feature points to obtain an optimal search order; obtaining the altitude of a pilot parafoil when it passes through each two-dimensional feature point based on time estimation to obtain three-dimensional spatial path points; segmenting the airdrop trajectory between each two adjacent three-dimensional spatial path points with the lowest energy consumption as the optimization goal, and integrating the trajectory planning results of the pilot parafoil; obtaining a terrain image transmitted back by the pilot parafoil; classifying the terrain image based on a terrain classification model, eliminating all nominal landing points that are unsuitable for landing, and outputting the remaining nominal landing points. The present invention is applied to the field of trajectory planning, can effectively reduce the energy consumption of the pilot parafoil, and can also perform qualitative and quantitative analysis and evaluation of a large range of pre-selected landing areas.

Description

Technical Field

[0001] The present invention relates to the technical field of trajectory planning, in particular to a method for planning a homing landing point for a parafoil system based on terrain and landforms. Background Art

[0002] During airdrop missions, disrupted ground communications and altered or missing terrain information in the target area can lead to lost supplies, personnel injuries, and other potential issues, ultimately resulting in mission failure. Therefore, selecting a suitable landing site is paramount for airdrop operations, with terrain safety being a key consideration. To address this, obtaining terrain information about the target area is essential to screen landing sites in advance.

[0003] Existing landing point selection methods fall into two main categories. One involves online selection during the landing process. During landing, the aircraft uses sensors such as lidar and cameras to acquire data such as elevation and landing point images, and then uses a designed algorithm to calculate the optimal landing point in real time. However, these methods have drawbacks: purely online selection methods require a large amount of image data and computational complexity, and cannot determine the landing point in a timely manner when the landing area is too large. Pre-flight offline selection, on the other hand, uses statistical and distribution analysis of the terrain to select macro-regions, but lacks quantitative analysis of the terrain and cannot respond to sudden and drastic changes in the terrain. Therefore, applying image recognition technology to acquire terrain information in real time is essential for airdrop and landing missions. Pre-screening landing points using existing satellite maps and then verifying their feasibility using online selection methods ensures both real-time performance and high efficiency.

[0004] A "pilot-type" parafoil is a type of parafoil that is deployed first for advance tasks such as terrain exploration and wind field measurement before subsequent large-scale airdrops on high-value targets. It can obtain the environmental characteristics of the target area in real time, providing reference situation information and homing routes for the deployment of the cluster. For the homing trajectory planning of the pilot-type parafoil system, the current mainstream methods include the simple homing method, the optimal control homing method based on controlling minimum energy, and the segmented homing method. The optimal control homing method has low energy consumption, but the control law changes continuously, making it difficult to implement. The conventional segmented homing method, although simpler to implement, has relatively high energy consumption. Summary of the Invention

[0005] In response to the above-mentioned deficiencies in the prior art, the present invention provides a homing landing point planning method for a parafoil system based on terrain and landforms, which can not only effectively reduce the energy consumption of the pilot parafoil, but also perform qualitative and quantitative analysis and evaluation of a large range of pre-selected landing areas.

[0006] To achieve the above-mentioned object, the present invention provides a method for planning a homing point for a parafoil system based on terrain and features, comprising the following steps:

[0007] Step 1: Analyze the digital elevation map of the planned landing area based on the terrain analysis model to select all nominal landing points with terrain undulations suitable for parafoil landing;

[0008] Step 2: Filter out a number of two-dimensional feature points at all nominal landing points. The filtering rule is: when the pilot parafoil passes through all the two-dimensional feature points, it can scan all the nominal landing points.

[0009] Step 3: Sort all the two-dimensional feature points to obtain the optimal search order;

[0010] Step 4, based on the optimal search sequence, obtain the height of the pilot parafoil when it passes through each two-dimensional feature point based on time estimation, and obtain a three-dimensional space path point corresponding to each two-dimensional feature point;

[0011] Step 5: Segment the airdrop trajectory between each two adjacent three-dimensional space path points, and plan each segmented trajectory with the lowest energy consumption as the optimization goal, while satisfying the constraints, and integrate the trajectory planning results of the pilot parafoil;

[0012] Step 6, controlling the pilot parafoil to scan the surface features according to the trajectory planning result, and obtaining the images of the features transmitted back by the pilot parafoil;

[0013] Step 7: classify the object image based on the object classification model, eliminate all nominal landing points that are not suitable for landing, and output the remaining nominal landing points.

[0014] In one embodiment, in step 1, the terrain analysis model uses an inverse pyramid landing point selection algorithm to screen and obtain all nominal landing points;

[0015] Among them, the inverse pyramid landing point selection algorithm searches the pre-landing area in a layer-by-layer order of flat point search, safe landing point search, reliable obstacle avoidance point search, safe landing area and nominal landing point area selection.

[0016] In one embodiment, in step 2, the step of selecting a plurality of two-dimensional feature points from all nominal landing points is as follows:

[0017] Step 201, add all nominal landing points to the point set;

[0018] Step 202: randomly select a point in the point set as a two-dimensional feature point, and draw a circle with the selected two-dimensional feature point as the center and r as the radius;

[0019] In step 203 , all points in the circle created in step 202 are deleted from the point set, and steps 202 to 203 are performed again until the point set is an empty set.

[0020] In one embodiment, in step 3, the optimal search order is obtained based on a simulated annealing algorithm with the goal of minimizing the sum of the distances between adjacent two-dimensional feature points in the search order. The process is as follows:

[0021] Step 301, obtaining an initial temperature T0, an annealing temperature ΔT, and a randomly set initial search order, and setting the current temperature T=T0;

[0022] Step 302, let T = T - ΔT, and calculate the sum L0 of the line distances between adjacent two-dimensional feature points in the current search order;

[0023] Step 303: Randomly select two nodes in the current search order and exchange their positions to obtain a new search order, and calculate the sum L of their connection distances;

[0024] Step 304: Determine whether L0>L+exp(α·(T0-T) / T0) holds true, where α is a control parameter:

[0025] If so, the new search order is used as the current search order and step 305 is performed;

[0026] Otherwise, proceed to step 305;

[0027] Step 305: Determine whether T<0 holds:

[0028] If so, take the current search order as the optimal search order and output it;

[0029] Otherwise, return to step 302.

[0030] In one embodiment, in step 3, the height of the pilot parafoil when it passes through each two-dimensional feature point is estimated based on time, specifically:

[0031]

[0032] Among them, h i 、h i-1 is the height of the pilot parafoil when it passes through the i-th and i-1-th two-dimensional feature points, τ i-1,i is the estimated time for the pilot parafoil to maneuver from the i-1th two-dimensional feature point to the i-th two-dimensional feature point, ∑τ i-1,i is the sum of all estimated times, h0 is the initial position height of the pilot parafoil, and h min ≤h0≤h max , h min is the minimum scanning height of the pilot parafoil, h max It is the highest scanning altitude of the pilot parafoil.

[0033] In one embodiment, the estimated time for the pilot parafoil to maneuver from the i-1th two-dimensional feature point to the i-th two-dimensional feature point is specifically:

[0034]

[0035] Among them, l i-1,i is the plane distance between the i-1th two-dimensional feature point and the i-th two-dimensional feature point, V is the velocity modulus of the pilot parafoil, γ is the flight path angle of the pilot parafoil, Δξ i-2,i-1,i is the maneuvering angle of the line between the i-2th two-dimensional feature point and the i-1th two-dimensional feature point relative to the line between the i-1th two-dimensional feature point and the i-th two-dimensional feature point, u max It is the maximum control amount of the pilot parafoil.

[0036] In one embodiment, the minimum scanning altitude and the maximum scanning altitude of the pilot parafoil are:

[0037] h min =v / tan(θ / 2)

[0038]

[0039] Where v is the minimum length scale, θ is the maximum field of view of the camera on the pilot parafoil, and px is scanner The camera resolution of the pilot parafoil, px model The image resolution for image recognition.

[0040] In one embodiment, in step 5, when planning the segmented trajectory, the segmented trajectory is divided into N segments in the height direction, and a control amount u is applied between each segment. ij Perform a maneuver;

[0041] The goal in planning the segmented trajectory is to obtain a set of optimal control quantities U i =[u i2 ,…u ij ,…,u iN ], so that the energy consumption is minimized. First, the particle swarm algorithm is used to obtain the approximate range of the optimal control quantity, and then the interior point method-obstacle penalty function method is used to obtain the optimal control quantity U within the range. The specific steps include:

[0042] Step 501, set the number of segments M, the segment number counter p=1, the allowable function critical value J0, the iteration counter T=1, the particle swarm iteration limit T0, the global optimal weight C1 and the local optimal weight C2;

[0043] Step 502: Based on the parafoil coordinate position and direction angle, the optimization function J of the p-th segment trajectory is given. p ;

[0044] Step 503: Randomly initialize the pth segment trajectory input Particle evolution speed Among them, k is the particle number, p is initially 1, and u p2 ,…,u pN The value range is [-u max ,u max ],v p2 ,…,v pN The value range is [-u max ,u max ],u max It is the maximum control amount of the pilot parafoil;

[0045] Step 504, update renew 1, where U p0 is the optimal solution for the p-th trajectory, is the local optimal solution of the kth particle in the pth trajectory;

[0046] Step 505, determine |u pj |>u max Is it true, where j = 2, 3, 4, ..., N:

[0047] If so, let u pj in [-u max ,u max ] randomly select values;

[0048] Otherwise, make u pj The value remains unchanged;

[0049] Step 506, determine |v pj |>u max Is it true, where j = 2, 3, 4, ..., N:

[0050] If so, let v pj in [-u max ,u max ] randomly select values;

[0051] Otherwise, make v pj The value remains unchanged;

[0052] Step 507, determine Is it established?

[0053] If so, then

[0054] Otherwise, make The value remains unchanged;

[0055] Step 508, determine Whether it holds:

[0056] If so, then let

[0057] Otherwise, keep the value of U p0 unchanged;

[0058] Step 509, determine whether J p (U p0 ) < J0 or T > T0 holds;

[0059] If so, then let U p = U p0 and then proceed to Step 5010;

[0060] Otherwise, return to Step 504;

[0061] Step 5010, perform minimization of J p (U p ) + μ·c(U p ) using the Newton method, where μ is the penalty factor and c(U p ) is the out-of-bounds penalty function;

[0062] Step 5011, update p = p + 1 and determine whether p > M is satisfied:

[0063] If so, then output the control matrix U = [U1; U2; …; U M "

[0064] Otherwise, return to Step 502.

[0065] In one embodiment, in Step 5, the constraint conditions include boundary constraints, headwind landing constraints, control constraints, and field-of-view angle constraints.

[0066] In one embodiment, in Step 7, the ground object classification model is composed of a trained ResNet34 network.

[0067] Compared with the prior art, the present invention has the following beneficial technical effects:

[0068] 1. The present invention addresses the problem of landing point selection during the homing process of the parafoil system, combines the impacts of terrain and ground objects on landing, and makes a dual selection of the landing point from two perspectives: terrain undulation and ground object screening, effectively avoiding the two problems of insufficient computing power resulting in failure to obtain results in a timely manner and insufficient real-time image causing drastic changes in the landing point terrain and inability to land.

[0069] 2. The present invention can meet the requirements of real-time task processing and has high accuracy, providing a useful reference for the landing point selection of the parafoil system and the leading parafoil trajectory planning based on the landing point selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0071] Figure 1 This is a flow chart of a method for planning a homing point for a parafoil system based on terrain and features in an embodiment of the present invention;

[0072] Figure 2 Schematic diagram of the estimated maneuvering time between two-dimensional feature points in an embodiment of the present invention;

[0073] Figure 3 Schematic diagram of the field of view in an embodiment of the present invention;

[0074] Figure 4 This is a contour map of the pre-landing area in an embodiment of the present invention;

[0075] Figure 5 Schematic diagram of the simulation results of the terrain analysis model in an embodiment of the present invention;

[0076] Figure 6 Schematic diagram of the distribution of nominal landing points and two-dimensional feature points in an embodiment of the present invention;

[0077] Figure 7 Schematic diagram of the optimization result of the search order of two-dimensional feature points in an embodiment of the present invention;

[0078] Figure 8 This is a trajectory diagram of a pilot parafoil in an embodiment of the present invention;

[0079] Figure 9 This is a plane projection diagram of the trajectory of the pilot parafoil in an embodiment of the present invention;

[0080] Figure 10 This is a graph showing changes in control amounts for a pilot parafoil according to an embodiment of the present invention;

[0081] Figure 11 Schematic diagram of a simulation prediction atlas in an embodiment of the present invention.

[0082] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0083] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0084] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0085] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0086] like Figure 1 The present embodiment discloses a method for planning a homing point for a parafoil system based on terrain and features, which mainly includes the following steps:

[0087] Step 1: Analyze the digital elevation map of the planned landing area based on the terrain analysis model to select all nominal landing points with terrain undulations suitable for parafoil landing;

[0088] Step 2: Filter out a number of two-dimensional feature points at all nominal landing points. The filtering rule is: when the pilot parafoil passes through all the two-dimensional feature points, it can scan all the nominal landing points.

[0089] Step 3: Sort all the two-dimensional feature points to obtain the optimal search order;

[0090] Step 4: Based on the optimal search sequence, the height of the pilot parafoil when it passes through each two-dimensional feature point is obtained based on time estimation, and a three-dimensional space path point corresponding to each two-dimensional feature point is obtained;

[0091] Step 5: Segment the airdrop trajectory between each two adjacent three-dimensional space path points, and plan each segmented trajectory with the lowest energy consumption as the optimization goal, while satisfying the constraints, and integrate the trajectory planning results of the pilot parafoil;

[0092] Step 6: Control the pilot parafoil to scan the surface features according to the trajectory planning result, and obtain the images of the features sent back by the pilot parafoil;

[0093] Step 7: classify the object image based on the object classification model, eliminate all nominal landing points that are not suitable for landing, and output the remaining nominal landing points.

[0094] The soft landing capability of a parafoil swarm system primarily encompasses three aspects: landing point accuracy, autonomous obstacle avoidance, and terrain slope adaptability. Therefore, the terrain analysis model in this embodiment utilizes an inverse pyramid landing point selection algorithm to screen all nominal landing points. This algorithm progressively selects points in the pre-landing area, following the sequence of searching for flat points, safe landing points, reliable obstacle avoidance points, and finally, selecting safe landing areas and nominal landing areas. This algorithm first searches for flat points within the pre-landing area, then searches for safe landing points within the flat points, then searches for reliable obstacle avoidance points within the safe landing points, and finally searches for safe landing areas and nominal landing areas within the reliable obstacle avoidance points. The algorithm gradually converges from weak to strong, narrowing the landing point selection area.

[0095] Considering the field of view constraints of a pilot parafoil when scanning downward from high altitude, it cannot scan all nominal landing points. Furthermore, requiring the parafoil to pass over every nominal landing point would consume excessive energy. Therefore, this embodiment selects a portion of two-dimensional feature points from all nominal landing points, allowing the pilot parafoil to scan the terrain features of all nominal landing points within a specific area when passing over the two-dimensional feature points. The specific implementation process is as follows:

[0096] Step 201, add all nominal landing points to the point set;

[0097] Step 202: randomly select a point in the point set as a two-dimensional feature point, and draw a circle with the selected two-dimensional feature point as the center and r as the radius;

[0098] In step 203, all points in the circle drawn in step 202 are deleted from the point set, and steps 202 to 203 are performed again until the point set is an empty set.

[0099] Since there are often multiple two-dimensional feature points, the order in which the pilot parafoil searches for the two-dimensional feature points during the homing process will affect the minimum launch height and the energy consumption required for the parafoil. In addition, since the total energy of the parafoil is limited, it is necessary to optimize the search order of all two-dimensional feature points. In this embodiment, the optimization sorting goal is to minimize the sum of the distances between the connecting lines of the two-dimensional feature points, that is:

[0100]

[0101] In the formula, (x i ,y i ) is the plane position coordinate of the i-th two-dimensional feature point, and n is the number of two-dimensional feature points;

[0102] In the specific implementation process, this embodiment adopts the simulated annealing algorithm to obtain the optimal search order, and the process includes the following steps:

[0103] Step 301, obtaining an initial temperature T0, an annealing temperature ΔT, and a randomly set initial search order, and setting the current temperature T=T0;

[0104] Step 302, let T = T - ΔT, and calculate the sum L0 of the line distances between adjacent two-dimensional feature points in the current search order;

[0105] Step 303: Randomly select two nodes in the current search order and exchange their positions to obtain a new search order, and calculate the sum L of their connection distances;

[0106] Step 304: determine whether L0>L+exp(α·(T0-T) / T0) holds true. α is a control parameter. The specific value of the control parameter α is adjustable. For example, the value may be α=7:

[0107] If so, the new search order is used as the current search order and step 305 is performed;

[0108] Otherwise, proceed to step 305;

[0109] Step 305: Determine whether T<0 holds:

[0110] If so, take the current search order as the optimal search order and output it;

[0111] Otherwise, return to step 302.

[0112] This embodiment uses total route distance as the search target for the optimal search order. A route is randomly selected in advance and optimized using an annealing algorithm to find a path with the minimum total distance among all routes. Since the function values ​​of the two endpoints of a route are equal in the simulated annealing algorithm, and any starting point can minimize the path, it is sufficient to randomly select one of the two endpoints as the starting point.

[0113] After obtaining the optimal search order, the trajectory of the pilot parafoil can be planned to minimize energy consumption and ensure that the parafoil passes through all two-dimensional feature points while satisfying the constraints, thereby scanning all nominal landing points. To meet the path constraints, this embodiment designs a parafoil trajectory adaptive segmentation strategy based on time estimation. The strategy process is as follows:

[0114] First, the pilot parafoil is launched from one end point of the optimal search sequence, and the first two-dimensional feature point is scanned. Then, the time required to fly to the next two-dimensional feature point is estimated. The initial direction is set from the starting point to the next two-dimensional feature point. The estimated time for the pilot parafoil to maneuver from the i-1th two-dimensional feature point to the i-th two-dimensional feature point is:

[0115]

[0116] Among them, τ i-1,i is the estimated time for the pilot parafoil to maneuver from the i-1th two-dimensional feature point to the i-th two-dimensional feature point, l i-1,i is the plane distance between the i-1th two-dimensional feature point and the i-th two-dimensional feature point, V is the total velocity of the pilot parafoil, γ is the track angle of the pilot parafoil, Δξ i-2,i-1,i is the maneuvering angle of the line between the i-2th two-dimensional feature point and the i-1th two-dimensional feature point relative to the line between the i-1th two-dimensional feature point and the i-th two-dimensional feature point, i max It is the maximum control amount of the pilot parafoil;

[0117] Assume that the horizontal distance between two-dimensional feature point 2 and two-dimensional feature point 3 is l 2,3 , the line connecting the two-dimensional feature point 1 and the two-dimensional feature point 2, the maneuvering angle relative to the two-dimensional feature point 2 and the two-dimensional feature point 3 is Δξ, and the maneuvering angle is defined as Figure 2 As shown, the theoretical minimum time required for it to fly from two-dimensional feature point 2 to two-dimensional feature point 3 can be estimated as:

[0118]

[0119] Secondly, the minimum scanning height constraint: According to the field of view constraint of the pilot parafoil, the minimum scanning height is estimated to ensure that the scanned image resolution can meet the image recognition requirements. Therefore, the minimum scanning height h of the pilot parafoil is min It can be estimated as:

[0120] h min =v / tan(θ / 2)

[0121] Where v is the minimum length scale in the terrain analysis model, and θ is the maximum field of view of the camera on the pilot parafoil;

[0122] Then, the maximum scanning altitude constraint: Considering that if the parafoil flies at a high altitude, it is impossible to accurately identify the ground objects in the ground area, it is also necessary to specify its maximum flight altitude. According to the field of view angle constraint, the maximum scanning altitude of the pilot parafoil h max for:

[0123]

[0124] Among them, px scanner The camera resolution of the pilot parafoil, px model Image resolution for image recognition;

[0125] Finally, fix the position of the two-dimensional feature points and segment the trajectory: According to the estimated maneuvering time and altitude constraint, the position of each two-dimensional feature point in the altitude direction can be fixed as follows:

[0126]

[0127] Among them, h i 、h i-1 is the height of the pilot parafoil when it passes through the i-th and i-1-th two-dimensional feature points, ∑τ i-1,i is the sum of all estimated times, h0 is the initial position height of the pilot parafoil;

[0128] Combining the plane coordinates of each two-dimensional feature point and the calculated height of the pilot parafoil when it passes through the two-dimensional feature point, the three-dimensional coordinates of a series of three-dimensional space path points during the pilot parafoil's homing process can be determined. The three-dimensional space path points correspond one-to-one to the two-dimensional feature points. The pilot parafoil is controlled to pass through each three-dimensional space path point in sequence according to the optimal search order. According to each three-dimensional space path point, track segmentation based on estimated time can be achieved.

[0129] In this embodiment, the four-degree-of-freedom motion equation of the pilot parafoil in the inertial coordinate system is:

[0130]

[0131] Among them, ξ is the velocity direction angle, u is the control input parameter, V s is the horizontal projection velocity, V z is the vertical projection velocity, w x is the wind speed component along the x-axis, w y is the wind speed component along the y-axis, is the time derivative of the three-axis components of displacement in the inertial reference frame Oxyz;

[0132] When a piloted parafoil performs a given mission, in addition to meeting the parafoil's homing constraints, maneuverability constraints, and headwind landing constraints, it also needs to meet the field of view constraints of the carried camera to meet the needs of ground scanning.

[0133] The boundary constraints are as follows: when the pilot parafoil performs the scanning task, it is assumed that the state of each parafoil at the initial moment is known, that is, the initial conditions are known, where (x0, y0, z0) is the initial position of the pilot parafoil, ξ0 is the initial yaw angle of the pilot parafoil, and the plane coordinate state of the parafoil at the two-dimensional feature point is also known, where (xi ,y i ,z i ) is the coordinate of the ith two-dimensional feature point, and the terminal boundary condition is that the parafoil is at t f Arrival at the target landing point (x f ,y f ,z f );

[0134] The headwind landing constraint is: before the parafoil reaches the target landing point, it should try to ensure that the headwind is aligned, that is, the landing direction of the parafoil is opposite to the wind direction, so as to meet the bird landing condition. Assume that the wind direction angle near the target landing point is ξ w , in order to meet the headwind landing constraint, the azimuth angle of the parafoil during landing should satisfy ξ(t f )=ξ w ±(2k+1)π,

[0135] The control constraint is: during the maneuvering process of the parafoil system, the parachute rope is mainly pulled down to achieve the desired effect. However, the amount of pull-down of the left and right parachute ropes is limited. When the parachute ropes are pulled down to the maximum stable unilateral pull-down amount, the corresponding turning radius is the minimum. At this time, the turning angular velocity control amount is the maximum. Therefore, the yaw angular velocity control amount is constrained to |u|≤u max , where the maximum control quantity u allowed is max Corresponds to the minimum turning radius of the parafoil;

[0136] The field of view angle constraint is: when the pilot parafoil performs a scanning mission, it is necessary to use the camera carried to shoot and scan the ground, and the scanning area of ​​the camera is constrained by its field of view angle and flight altitude. The field of view angle refers to the angle formed by the two edges of the maximum range of the object image that can pass through the lens of the optical instrument, with the lens of the optical instrument as the vertex. Figure 3 , let the field of view angle be θ, and the height of the parafoil from the ground be h, then the area of ​​the observable range S = π(htanθ) 2 .

[0137] In the process of planning the segmented trajectory, considering the maneuverability and computational difficulty of the parafoil system, the descent trajectory between two three-dimensional space path points of the pilot parafoil is divided into N segments. For example, the segmented trajectory between the i-1th three-dimensional space path point and the i-th three-dimensional space path point is composed of N+1 segment nodes, where the 1st and N+1th segment nodes correspond to the i-1th and i-th three-dimensional space path points respectively, and a control amount u is applied between each segment. ij (j=2,…,N) performs a maneuver. The goal in planning the segmented trajectory is to obtain a set of optimal control quantities U for each segment. i =[u i2 ,…uij ,…,u iN ], so that the energy consumption is minimized, that is, the objective function is:

[0138]

[0139] Among them, J i-1,i is the objective function for segmented trajectory planning between the i-1th three-dimensional space path point and the i-th three-dimensional space path point, t j-1 , t j is the moment when it passes through the j-1th segment node and the jth segment node;

[0140] In this embodiment, a particle swarm optimization algorithm is combined with an interior point method-obstacle penalty function method to perform segmented trajectory planning, thereby obtaining the optimal control amount for each segment to prevent it from falling into a local optimal solution too early. The specific implementation process is as follows:

[0141] Step 501, set the number of segments M, the segment number counter p=1, the allowable function critical value J0, the iteration counter T=1, the particle swarm iteration limit T0, the global optimal weight C1 and the local optimal weight C2;

[0142] Step 502: Based on the parafoil coordinate position and direction angle, the optimization function J of the p-th segment trajectory is given. p ;

[0143] Step 503: Randomly initialize the pth segment trajectory input Particle evolution speed Among them, k is the particle number, p is initially 1, and u p2 ,…,u pN The value range is [-u max ,u max ],v p2 ,…,v pN The value range is [-u max ,u max ];

[0144] Step 504, update renew Among them, U p0 is the optimal solution for the p-th trajectory, is the local optimal solution of the kth particle in the pth trajectory;

[0145] Step 505, determine |u pj |>u max Is it true, where j = 2, 3, 4, ..., N;

[0146] If so, let u pj in [-u max,u max Take a random value from

[0147] Otherwise, keep the value of u pj unchanged;

[0148] Step 506, determine whether |v pj | > u max holds, where j = 2, 3, 4, …, N;

[0149] If so, let v pj take a random value in [-u max , u max ;

[0150] Otherwise, keep the value of v pj unchanged;

[0151] Step 507, determine whether ]](Uholds, where, represents the optimized function value when the input of the p-th trajectory segment is , represents the optimized function value when the input of the p-th trajectory segment is ;

[0152] If so, let <o:p><o:p>Otherwise, keep the value of unchanged;

[0154] Step 508, determine whether <00Uholds, where J p (U p0 ) represents the optimized function value when the input of the p-th trajectory segment is U p0 ;

[0155] If so, let

[0156] Otherwise, keep the value of U p0 unchanged;

[0157] Step 509, determine whether J p (U p0 ) < J0 or T > T0 holds;

[0158] If so, let U p = U p0 and then proceed to Step 5010; ​​​​​​​​​p ), where μ is the penalty factor, c(U p ) is the out-of-bounds penalty function;

[0161] Step 5011: Update p=p+1 and determine whether p>M:

[0162] If so, then the output control matrix U=[U1;U2;…;U M ]

[0163] Otherwise, return to step 502.

[0164] During the landing process of the parafoil system, not only the impact of the undulation of the terrain on the landing must be considered, but also the threat posed by the existing ground objects to a safe landing must be considered. During the landing mission, if the ground objects are found to be buildings, water areas, and woodlands, then the area is not suitable for the landing mission, and the landing point should be changed, and the area where the existing ground objects are roads or cultivated land should be selected as the landing point. Therefore, after the pilot parafoil is segmented and planned, the pilot parafoil scans the surface objects according to the trajectory planning results, and obtains the ground object images sent back by the pilot parafoil. The ground object images are then input into the ground object classification model for ground object classification, and all nominal landing points that are not suitable for landing are eliminated, such as the nominal landing points in areas such as buildings, water areas, and woodlands. The remaining nominal landing points are retained and output, so that more accurate landing point selection can be made, thereby improving the reliability of the homing mission.

[0165] In this example, to identify the types of features in the planned landing zone, we first needed to obtain images of typical features. We obtained several 256×256 satellite images with a resolution of 30m from a public source. We manually categorized these images into five basic features: woodland, cultivated land, water, buildings, and roads. This provided the training dataset. The amount of data for each feature was roughly equal. The dataset was divided into a training set and a validation set in a 7:1 ratio.

[0166] To select a suitable network model for object classification, we trained the constructed dataset using ResNet34, ResNet 101, VGG19, and GoogLeNet, commonly used in image classification. The training was performed using Python. Training was performed using the third-party deep learning platform Pytorch 2.1.2, an NVIDIA Tesla P100 GPU, and CUDA version 12.1, with 400 training iterations. The training times for the four network models are shown in Table 1, and the training results are shown in Table 2.

[0167] Table 1

[0168]

[0169] Table 2

[0170]

[0171] As shown in Table 1, the training time of the VGG and ResNet 101 network models is longer. The VGG network uses more parameters, which leads to a longer model training time. The ResNet network is deeper and uses more residual blocks, which leads to its fitting training time being an order of magnitude higher than the other three. As can be seen from Table 2, the accuracy, recall rate and F1 value of the ResNet34 network are all larger among the four network models. It has better predictability, lower loss function, better model fit and smaller error value. Comparing the prediction time, the ResNet 34 network has a shorter prediction time of less than 1s, which can make fast and accurate predictions and meet the basic task requirements of land object recognition. Based on the above evaluation and analysis, this embodiment selects the ResNet 34 network for land object classification.

[0172] The following further illustrates the method for planning the homing landing point of the parafoil system based on terrain and features in this embodiment in conjunction with specific numerical simulation analysis.

[0173] In order to verify the effectiveness of the method of this embodiment, it is necessary to select an area with undulating terrain for simulation verification. In this example, a square topographic map with a resolution of 30m and a size of 72px×72px, that is, a side length of 216m×216m, is selected. The corresponding latitude and longitude range is (28.5303°N, 112.3103°E) to (28.5500°N, 112.3300°E) DEM for simulation calculation. The data comes from the Shuttle Radar Topography Mission (SRTM) of the Resource and Environmental Science Data Center of the Chinese Academy of Sciences. The contour map of the area is as follows: Figure 4 shown.

[0174] The terrain analysis model is used to obtain the flat points, safe landing points, reliable obstacle avoidance points and nominal landing points for the simulation screening in the area. Figure 5 The parameter settings are shown in Table 3. Figure 5 It can be seen that the range of landing points in the area is gradually reduced, and finally reduced to the point where the black point is located. The black point in the figure is the working point where the terrain is suitable for carrying out the parafoil landing mission, that is, the nominal landing point.

[0175] Table 3

[0176]

[0177] The two-dimensional feature points are selected for the nominal landing points selected in the terrain analysis model. The selection results are as follows: Figure 6 As shown, the selected coverage radius r = 200m. Figure 6 In the figure, the black area is the nominal landing point selected by terrain analysis, the triangulated point is the two-dimensional feature point, and the circle represents the area covered by the two-dimensional feature point. Figure 6 It can be seen that the selection of two-dimensional feature points simplifies the task and ensures that the nominal landing points are scanned by the carried camera to meet the task requirements.

[0178] Then, the annealing algorithm is used to optimize the search order of each two-dimensional feature point, and the initial temperature is set to T = 1×10 7 , the optimization simulation results are as follows Figure 7 As shown in the figure, the endpoint in the upper right corner is selected as the starting point. The search order basically meets the goal of minimizing the connection distance.

[0179] Then the particle swarm algorithm is combined with the interior point method to solve the optimal trajectory planning problem. Figure 8 As shown, the plane projection of the trajectory is as follows Figure 9 As shown in the figure. The solid line is the parafoil trajectory, and the dotted line and arrows represent the search order. The control quantity u of each trajectory changes with time as shown in the figure. Figure 10 The planned horizontal projection of the flight path passes through each two-dimensional feature point, meeting the mission requirements of the pilot parafoil while controlling the discontinuous variation of the input quantity, making the actual operation relatively simple.

[0180] Finally, in order to verify the effectiveness of the ground feature analysis model, the ResNet34 network was used to test the ground features and identify the landforms of the mission landing area. A satellite map with a resolution of 30m was used, and the GPU used for prediction was NVIDIA Tesla P100. The prediction atlas was selected. Figure 11 , the recognition results are shown in Table 4. Figure 11 Compared with the results in Table 4, the accuracy of ground feature recognition is high, and the accuracy of the 20 recognized graphics reaches 100%. Figure 11 The average prediction time for each image is less than 1 second, meeting the requirements for real-time mission processing. Furthermore, based on the image recognition results, nominal landing sites unsuitable for landing missions can be eliminated, providing landing zone plans for large-scale airdrops. As shown in Table 4, landing zones numbered 1-8 were retained, while zones numbered 9-12 were accurately eliminated. This elimination of landing sites unsuitable for landing missions enables more accurate landing site selection and improves the reliability of the homing mission.

[0181] Table 4

[0182]

[0183] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for planning the homing point of a parafoil system based on terrain and features, characterized in that: The steps include: Step 1: Analyze the digital elevation map of the planned landing area based on the terrain analysis model to select all nominal landing points with terrain undulations suitable for parafoil landing; Step 2: Filter out a number of two-dimensional feature points at all nominal landing points. The filtering rule is: when the pilot parafoil passes through all the two-dimensional feature points, it can scan all the nominal landing points. Step 3: Sort all the two-dimensional feature points to obtain the optimal search order; Step 4, based on the optimal search sequence, obtain the height of the pilot parafoil when it passes through each two-dimensional feature point based on time estimation, and obtain a three-dimensional space path point corresponding to each two-dimensional feature point; Step 5: Segment the airdrop trajectory between each two adjacent three-dimensional space path points, and plan each segmented trajectory with the lowest energy consumption as the optimization goal, while satisfying the constraints, and integrate the trajectory planning results of the pilot parafoil; Step 6, controlling the pilot parafoil to scan the surface features according to the trajectory planning result, and obtaining the images of the features transmitted back by the pilot parafoil; Step 7: classify the object image based on the object classification model, eliminate all nominal landing points that are not suitable for landing, and output the remaining nominal landing points; In step 4, the height of the pilot parafoil when it passes through each two-dimensional feature point is obtained based on the time estimation, specifically: ； in, 、 For pilot paragliders 、 The height of the two-dimensional feature point, For pilot paragliders The two-dimensional feature points are moved to the The estimation time of two-dimensional feature points, is the sum of all estimated times, is the initial position height of the pilot parafoil, and , is the minimum scanning altitude of the pilot parafoil, It is the highest scanning altitude of the pilot parafoil; The pilot parafoil is composed of The two-dimensional feature points are moved to the The estimation time of a two-dimensional feature point is: ； in, For the The two-dimensional feature points and The plane distance between two-dimensional feature points, is the speed model of the pilot parafoil, is the flight path angle of the pilot parafoil, For the The two-dimensional feature points and The line between the two-dimensional feature points is relative to the The two-dimensional feature points and The maneuvering angle of the line connecting the two-dimensional feature points, It is the maximum control amount of the pilot parafoil; The minimum scanning altitude and the maximum scanning altitude of the pilot parafoil are respectively: ； ； in, is the minimum length scale, is the maximum field of view of the camera on the pilot parafoil, The resolution of the camera carried by the pilot parafoil is The image resolution for image recognition.

2. The method for planning the homing point of a parafoil system based on terrain and features according to claim 1, characterized in that: In step 1, the terrain analysis model uses an inverse pyramid landing point selection algorithm to screen all nominal landing points; Among them, the inverse pyramid landing point selection algorithm searches the pre-landing area in a layer-by-layer order of flat point search, safe landing point search, reliable obstacle avoidance point search, safe landing area and nominal landing point area selection.

3. The method for planning the homing point of a parafoil system based on terrain and features according to claim 1, characterized in that: In step 2, a number of two-dimensional feature points are selected from all nominal landing points, specifically: Step 201, add all nominal landing points to the point set; Step 202: randomly select a point in the point set as a two-dimensional feature point, and use the selected two-dimensional feature point as the center and the r Make a circle with radius In step 203 , all points in the circle created in step 202 are deleted from the point set, and steps 202 to 203 are performed again until the point set is an empty set.

4. The method for planning the homing landing point of a parafoil system based on terrain and features according to claim 1, characterized in that: In step 3, the goal is to minimize the sum of the distances between adjacent two-dimensional feature points in the search order. The optimal search order is obtained based on the simulated annealing algorithm. The process is as follows: Step 301: Get the initial temperature , annealing temperature And the initial search order is randomly set, and the current temperature ; Step 302: , and calculate the sum of the distances between adjacent two-dimensional feature points in the current search order ; Step 303: Randomly select two nodes in the current search order and exchange their positions to obtain a new search order, and calculate the sum of their connection distances. ; Step 304, determine Is it established, among which, For the control parameters: If so, the new search order is used as the current search order and step 305 is performed; Otherwise, proceed to step 305; Step 305, determine Is it established? If so, take the current search order as the optimal search order and output it; Otherwise, return to step 302.

5. The method for planning the homing landing point of a parafoil system based on terrain and features according to claim 1, 2, 3 or 4, characterized in that: In step 5, when planning the segmented trajectory, the segmented trajectory is divided into Segments, with a control amount applied between each segment Perform a maneuver; The goal in planning a segmented trajectory is to obtain a set of optimal control quantities. , so that the energy consumption is minimized. First, the particle swarm algorithm is used to obtain the approximate range of the optimal control quantity, and then the interior point method-obstacle penalty function method is used to obtain the optimal control quantity within the range. , which specifically includes the following steps: Step 501, set the number of segments , Segment counter , critical value of the admissible function , iteration counter , the upper limit of particle swarm iterations , global optimal weight and local optimal weights ; Step 502: Based on the parafoil coordinate position and direction angle, the first Optimization function for segmented trajectories ; Step 503, randomly initialize the Segment trajectory input , particle evolution speed ,in, is the particle number, The initial value is 1, The value range is , The value range is , It is the maximum control amount of the pilot parafoil; Step 504, update ,renew , ,in, For the The optimal solution of the segment trajectory, For the The first The local optimal solution of each particle; Step 505, determine Is it established, : If so, then exist Random value in Otherwise, make The value remains unchanged; Step 506, determine Is it established, : If so, then exist Random value in Otherwise, make The value remains unchanged; Step 507, determine Is it established? If so, then ; Otherwise, make The value remains unchanged; Step 508, determine Is it established? If so, then ; Otherwise, make The value remains unchanged; Step 509, determine or whether it is established; If so, then Then proceed to step 5010; Otherwise, return to step 504; Step 5010, minimizing using Newton's method ,in, is the penalty factor, is the out-of-bounds penalty function; Step 5011, Update , and judge whether it satisfies : If so, then the output control matrix ; Otherwise, return to step 502.

6. The method for planning the homing point of a parafoil system based on terrain and features according to claim 1, 2, 3 or 4, characterized in that: In step 5, the constraints include boundary constraints, headwind landing constraints, control constraints, and field of view constraints.

7. The method for planning the homing point of a parafoil system based on terrain and features according to claim 1, 2, 3 or 4, characterized in that: In step 7, the object classification model is composed of a trained ResNet34 network.

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